AI approaches on Earth observation data have the potential to fill in this gap. Several recent works have developed deep learning models to automatically interpret remotely sensed imagery and deploy them at scale to map infrastructure [1]}, [2]}, [3]}, [4]}. Methods for mapping methane source infrastructure have been emerging as well, including well pads in the Denver basin [5]}, oil refineries and concentrated animal feed operations in the U.S. [6]}, [7]}, and wastewater treatment plants in Germany [8]}. Each of these works depended on the curation of large, labeled datasets to develop the machine learning models, but there is a lack of publicly available, labeled Earth observation data, specifically on methane emitting infrastructure, which prohibits researchers and practitioners from building automated mapping approaches.
